Representing Monads with Capabilities

Programming with monads can be advantageous even in imperative languages with builtin support for side effects. However, in these languages composing monadic programs is different from composing side effecting imperative programs. This does not need to be the case, as already noticed by Filinski [1994]. We revive the well-known technique of monadic reflection in the context of modern programming languages with support for fibers, generators, or coroutines. In particular, we show how (layered) monadic reflection can be implemented in a stack safe manner and how effect safety can conveniently be approximated by capability passing.

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Aleksander Slawomir Boruch-Gruszecki, Jonathan Immanuel Brachthäuser, Martin Odersky
2021
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https://infoscience.epfl.ch/record/291935?ln=en

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Distributed Programming via Safe Closure Passing

Philipp Haller

Programming systems incorporating aspects of functional programming, e.g., higher-order func- tions, are becoming increasingly popular for large-scale distributed programming. New frameworks such as Apache Spark leverage functional techniques to provide high-level, declarative APIs for in- memory data analytics, often outperforming traditional “big data” frameworks like Hadoop MapRe- duce. However, widely-used programming models remain rather ad-hoc; aspects such as implemen- tation trade-offs, static typing, and semantics are not yet well-understood. We present a new asyn- chronous programming model that has at its core several principles facilitating functional processing of distributed data. The emphasis of our model is on simplicity, performance, and expressiveness. The primary means of communication is by passing functions (closures) to distributed, immutable data. To ensure safe and efficient distribution of closures, our model leverages both syntactic and type-based restrictions. We report on a prototype implementation in Scala. Finally, we present pre- liminary experimental results evaluating the performance impact of a static, type-based optimization of serialization.

2015

Programming with Variable Functions

Martin Odersky

What is a good method to specify and derive imperative programs? This paper argues that a new form of functional programming fits the bill, where variable functions can be updated at specified points in their domain. Traditional algebraic specification and functional programming are a powerful pair of tools for specifying and implementing domains of discourse and operations on them. Recent work on evolving algebras has introduced the function update in algebraic specifications, and has applied it with good success in the modelling of reactive systems. We show that similar concepts allow one to derive efficient programs in a systematic way from functional specifications. The final outcome of such a derivation can be made as efficient as a traditional imperative program with pointers, but can still be reasoned about at a high level. Variable functions can also play an important role in the structuring of large systems. They can subsume object-oriented programming languages, without incurring the latter's problems with pointer aliasing and modularity.

1998

Instant Pickles: Generating Object-Oriented Pickler Combinators for Fast and Extensible Serialization

Eugene Burmako, Philipp Haller, Martin Odersky

As more applications migrate to the cloud, and as “big data” edges into even more production environments, the performance and simplicity of exchanging data between compute nodes/devices is increasing in importance. An issue central to distributed programming, yet often under-considered, is serialization or pickling, i.e., persisting runtime objects by converting them into a binary or text representation. Pickler combinators are a popular approach from functional programming; their composability alleviates some of the tedium of writing pickling code by hand, but they don’t translate well to object-oriented programming due to qualities like open class hierarchies and subtyping polymorphism. Furthermore, both functional pickler combinators and popular, Java-based serialization frameworks tend to be tied to a specific pickle format, leaving programmers with no choice of how their data is persisted. In this paper, we present object-oriented pickler combinators and a framework for generating them at compile-time, called scala/pickling, designed to be the default serialization mechanism of the Scala programming language. The static generation of OO picklers enables significant performance improvements, outperforming Java and Kryo in most of our benchmarks. In addition to high performance and the need for little to no boilerplate, our framework is extensible: using the type class pattern, users can provide both (1) custom, easily interchangeable pickle formats and (2) custom picklers, to override the default behavior of the pickling framework. In benchmarks, we compare scala/pickling with other popular industrial frameworks, and present results on time, memory usage, and size when pickling/unpickling a number of data types used in real-world, large-scale distributed applications and frameworks.

2013

Programming language

A programming language is a system of notation for writing computer programs. Most programming languages are text-based formal languages, but they may also be graphical. They are a kind of computer

Imperative programming

In computer science, imperative programming is a programming paradigm of software that uses statements that change a program's state. In much the same way that the imperative mood in natural language

Functional programming

In computer science, functional programming is a programming paradigm where programs are constructed by applying and composing functions. It is a declarative programming paradigm in which function d